{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "98584ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f6ad4fee",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
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       "      <th>男跳远</th>\n",
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       "      <th>男体前屈</th>\n",
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       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel('./体测分数_男生.xls')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "edac96b3",
   "metadata": {},
   "outputs": [
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       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_excel('./体测分数_女生.xls')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dabe1902",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['男1000米跑']\n",
    "df1['男引体']\n",
    "df2['女800米跑']\n",
    "df2['女跳远']\n",
    "df1['体重']\n",
    "df2['体重']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9d97cdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x212415cb2c8>,\n",
       "  <matplotlib.lines.Line2D at 0x21241791bc8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2123e4d6e48>,\n",
       "  <matplotlib.lines.Line2D at 0x2124162c908>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21241838b48>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2123e4d6f08>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21240f9fcc8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=[df1['男1000米跑']]\n",
    "lables = ['A']\n",
    "l = plt.boxplot(data,3,'gD',labels=lables)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1076df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='Kaiti SC'\n",
    "labels =[\"优秀\",\"合格\",\"不及格\"] # 标签\n",
    "percent = [95,261,105,30,9] # 某市星级酒店数量\n",
    "# 设置图⽚⼤⼩和分辨率\n",
    "fig=plt.figure(figsize=(5,5), dpi=150)\n",
    "# 偏移中⼼量，突出某⼀部分\n",
    "explode = (0, 0.1, 0, 0, 0)\n",
    "# 绘制饼图：autopct显示百分⽐，这⾥保留⼀位⼩数；shadow控制是否显示阴影\n",
    "plt.pie(x = percent, # 数据\n",
    " explode=explode, # 偏移中⼼量\n",
    " labels=labels, # 显示标签\n",
    " autopct='%0.1f%%', # 显示百分⽐\n",
    " shadow=True) # 阴影，3D效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "29b7a6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['男1000米跑等级'] = pd.cut(df1['男1000米跑'],bins=3,labels=['不及格','及格','优秀'],right=True)\n",
    "pd.cut(df1['男1000米跑'],bins=3,labels=['不及格','及格','优秀'],right=True).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "257f4a4c",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21512 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21512 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='YouYuan'\n",
    "labels =[\"优秀\",\"合格\",\"不及格\"]\n",
    "percent = [261,195,21]\n",
    "fig=plt.figure(figsize=(5,5), dpi=150)\n",
    "explode = (0, 0.1, 0)\n",
    "bingtu = plt.pie(x =percent,\n",
    "explode=explode,\n",
    "labels=labels,\n",
    "autopct='%0.2f%%',\n",
    "shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "63bdb528",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['男引体等级'] = pd.cut(df1['男引体'],bins=3,labels=['不及格','及格','优秀'],right=True)\n",
    "pd.cut(df1['男引体'],bins=3,labels=['不及格','及格','优秀'],right=True).value.counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7ea08fef",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21512 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21512 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='YouYuan'\n",
    "labels =[\"优秀\",\"合格\",\"不及格\"]\n",
    "percent = [20,230,227]\n",
    "fig=plt.figure(figsize=(5,5), dpi=150)\n",
    "explode = (0, 0.1, 0)\n",
    "bingtu = plt.pie(x =percent,\n",
    "explode=explode,\n",
    "labels=labels,\n",
    "autopct='%0.2f%%',\n",
    "shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6dd963a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['女800米跑等级'] = pd.cut(df2['女800米跑'],bins=4,labels = ['优秀', '及格', '一般', '不及格'] ,right=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4551b18f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "优秀     588\n",
       "不及格      5\n",
       "及格       0\n",
       "一般       0\n",
       "Name: 女800米跑, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df2['女800米跑'],bins=4,labels = ['优秀', '及格', '一般', '不及格'] ,right=True).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0087e829",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 33324 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20248 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 31168 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 21450 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 33324 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 19981 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['优秀', '及格', '一般', '不及格']\n",
    "percent = [588,5,0,0]\n",
    "zhifangtu = plt.bar(labels,percent,width = 0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "549db575",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-82-5dc7f857fe2a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-82-5dc7f857fe2a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    [for i in df2['女800米跑分数']]\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def convert(x):\n",
    "    if x <16.5\n",
    "        return '低体重'\n",
    "    elif x >=16.5 and x <= 23.2:\n",
    "        return '正常'\n",
    "    elif x .>=23.3 and x <26.3:\n",
    "        return '超重'\n",
    "    else:\n",
    "        return '肥胖'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5645f133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     355\n",
       "超重      58\n",
       "肥胖      38\n",
       "低体重     15\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['等级'] = pd.cut(df1['BMI'],bins=[0,16.5,23.3,26.4,50],labels=['低体重','正常','超重','肥胖'],right=True)\n",
    "pd.cut(df1['BMI'],bins=[0,16.5,23.3,26.4,50],labels=['低体重','正常','超重','肥胖'],right=True).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2212c58d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20302 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20307 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 36229 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32933 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32982 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20302 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20307 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 36229 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32933 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32982 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='YouYuan'\n",
    "labels =['低体重','正常','超重','肥胖']\n",
    "percent = [15,38,58,355]\n",
    "fig=plt.figure(figsize=(5,5), dpi=150)\n",
    "explode = (0, 0.1, 0, 0)\n",
    "bingtu = plt.pie(x =percent,\n",
    "explode=explode,\n",
    "labels=labels,\n",
    "autopct='%0.2f%%',\n",
    "shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7c93e4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     449\n",
       "超重      82\n",
       "肥胖      44\n",
       "低体重     11\n",
       "Name: BMI, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['等级'] = pd.cut(df2['BMI'],bins=[0,16.5,22.8,25.3,50],labels=['低体重','正常','超重','肥胖'],right=True)\n",
    "pd.cut(df2['BMI'],bins=[0,16.5,22.8,25.3,50],labels=['低体重','正常','超重','肥胖'],right=True).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "4fc79ef7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20302 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 20307 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 36229 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32933 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:240: RuntimeWarning: Glyph 32982 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20302 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 20307 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 37325 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 24120 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 36229 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32933 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "c:\\users\\loushang\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:203: RuntimeWarning: Glyph 32982 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='YouYuan'\n",
    "labels =['低体重','正常','超重','肥胖']\n",
    "percent = [11,44,82,449]\n",
    "fig=plt.figure(figsize=(5,5), dpi=150)\n",
    "explode = (0, 0.1, 0, 0)\n",
    "bingtu = plt.pie(x =percent,\n",
    "explode=explode,\n",
    "labels=labels,\n",
    "autopct='%0.2f%%',\n",
    "shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8794e30d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
